The world of retails as we know it is transforming right in front of our very eyes thanks to artificial intelligence (AI). However, not all of those changes are being welcomed.
Consumers as a whole are spending less on clothing and this is a problem for retailers. To add to this issue, rent prices are on the rise and product cycles are getting shorter, allowing these fast-fashion retailers to snap up the market share.
Many retail establishments have closed over the last 10 years, but not through any real fault of their own. The industry has changed.
The way people shop has changed. But do not fear, as this is where AI comes in, starting with big data.
READ MORE: 10 Powerful Applications of Artificial Intelligence in Retail
Through the emergence of the internet and e-commerce, companies can now use programs to analyze things such as what they’re competitors are selling and when exactly new trends are coming in.
Data scientists now play a vital role in retail as they’re responsible for organizing utilizing all of this worldwide apparel data.
They provide personalized clothing suggestions for the customers of North Face; they help automate recommendations for StitchFix; and they provide retailers with real-time updates as to when any products are sold out, new in, get discounted, anywhere in the world.
This enables retailers to make sure they have the right products for their target market; that essential stock will never run out, and everything is priced ideally to sell.
Through the help of AI and machine learning, retailers can utilize these market data to better understand their customers and to anticipate their buying trends.
ASOS, for example, is an online fashion retailer that uses machine learning techniques to recommend similar products to its customers. It also uses machine learning to analyze which items and sizes are most often returned to reduce costs and improve the customer journey.
The next hurdle for retailers is to somehow categorize their products. Data scientists need to develop AI that can not just understand the language of apparel but to actually view what’s in a photograph or image. It needs to be able to distinguish the different parts of the picture and differentiate it from the item of clothing being retailed.
This could involve separating a belt from jeans, or being able to detect what was technical sportswear and what was athleisure wear.
Data scientists’ next challenge involving AI is to use deep learning and neural networks to predict what will come next. AI is being integrated across the whole product’s lifecycle in one way or another.
And if retailers wish to continue being successful they must learn to tap into any upcoming AI developments in order to keep up with the rest of this fast-changing market.
Original source RTInsight